Head pose-free eye gaze prediction for driver attention study

Zhao, Tongtong; Ding, Xueyan; Bian, Jiming; Fu, Xianping

doi:10.1109/bigcomp.2017.7881713

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…Although at first focused on predicting the emotional state of people [12,13], as Facial Expression Recognition (FER) systems gained momentum and started achieving acceptable prediction accuracy, recent research papers have begun using facial features analysis for more complex tasks, such as tracking and predicting eye gaze [14,15], predicting driver attention for car accident prevention [14,16], predicting stress levels [2,17], diagnosing depression [3], assessing the facial attractiveness of individuals [18], evaluating people's trust [19], and predicting personality traits [4,[20][21][22][23]. All these research studies showed that the face indeed conveys information that can be analyzed to predict different psychological features of an individual.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

Gavrilescu

Vizireanu

2017

J Image Video Proc.

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We propose a novel three-layered neural network-based architecture for predicting the Sixteen Personality Factors from facial features analyzed using Facial Action Coding System. The proposed architecture is built on three layers: a base layer where the facial features are extracted from each video frame using a multi-state face model and the intensity levels of 27 Action Units (AUs) are computed, an intermediary level where an AU activity map is built containing all AUs' intensity levels fetched from the base layer in a frame-by-frame manner, and a top layer consisting of 16 feed-forward neural networks trained via backpropagation which analyze the patterns in the AU activity map and compute scores from 1 to 10, predicting each of the 16 personality traits. We show that the proposed architecture predicts with an accuracy of over 80%: warmth, emotional stability, liveliness, social boldness, sensitivity, vigilance, and tension. We also show there is a significant relationship between the emotions elicited to the analyzed subjects and high prediction accuracy obtained for each of the 16 personality traits as well as notable correlations between distinct sets of AUs present at high-intensity levels and increased personality trait prediction accuracy. The system converges to a stable result in no more than 1 min, making it faster and more practical than the Sixteen Personality Factors Questionnaire and suitable for real-time monitoring of people's personality traits.

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Section: Introductionmentioning

confidence: 99%

Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

Gavrilescu

Vizireanu

2017

J Image Video Proc.

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“…The majority of eye movements in the driving scenarios are accompanied by variation in head movement. Many existing works divide the gaze region in front of the driver into several gaze zones, and convert the gaze estimation into gaze zone estimation [29][30][31][32][33]. The gaze zone estimation outputs rough gaze prediction results, which is practical and applicable.…”

Section: Driver's Gaze Zone Estimationmentioning

confidence: 99%

“…Existing works predict the driver's eye gaze by regressing the facial features or pupil features to the gaze location or gaze angle [30,31,36,37]. The regression models are built by recent advanced techniques.…”

Section: Driver's Gaze Zone Estimationmentioning

confidence: 99%

Driver’s Head Pose and Gaze Zone Estimation Based on Multi-Zone Templates Registration and Multi-Frame Point Cloud Fusion

Yuan

2022

Sensors

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Head pose and eye gaze are vital clues for analysing a driver’s visual attention. Previous approaches achieve promising results from point clouds in constrained conditions. However, these approaches face challenges in the complex naturalistic driving scene. One of the challenges is that the collected point cloud data under non-uniform illumination and large head rotation is prone to partial facial occlusion. It causes bad transformation during failed template matching or incorrect feature extraction. In this paper, a novel estimation method is proposed for predicting accurate driver head pose and gaze zone using an RGB-D camera, with an effective point cloud fusion and registration strategy. In the fusion step, to reduce bad transformation, continuous multi-frame point clouds are registered and fused to generate a stable point cloud. In the registration step, to reduce reliance on template registration, multiple point clouds in the nearest neighbor gaze zone are utilized as a template point cloud. A coarse transformation computed by the normal distributions transform is used as the initial transformation, and updated with particle filter. A gaze zone estimator is trained by combining the head pose and eye image features, in which the head pose is predicted by point cloud registration, and the eye image features are extracted via multi-scale spare coding. Extensive experiments demonstrate that the proposed strategy achieves better results on head pose tracking, and also has a low error on gaze zone classification.

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“…Boverie and Giralt (2008) described a driver vigilance monitoring system developed by Continental Automotive, the improvement of which made the implementation of in-vehicle driver monitoring systems assessing the evolution of driver's state in real time possible (Boverie and Giralt 2008). Wang et al (2017) raised an appearance-based head pose-free eye gaze prediction method for driver gaze zone estimation under free head movements (Wang et al 2017). Park et al (2019) intended to develop accurate prediction models of a driver's hip location and eye location based on their statistical geometric relationships with anthropometric dimensions and driving postures (Park et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Prediction of the distribution of tractor driver’s eye positions

Huang¹,

Chen²,

Qiu³

et al. 2023

Meas. Sci. Technol.

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A predictive model of tractor driver’s eye positions based on the anthropometric parameters and tractor cab layout parameters was established using the statistical approach. To our knowledge, this model is the first published predictive model of tractor drivers’ eye positions that takes account of their body dimensions and tractor types. In order to predict the distribution of the drivers’ eye positions precisely, the fore-and-aft positions of the driving seat determined by 180 tractor drivers in three different tractors and the relative positions of the eyes to the designated reference points were recorded to quantitatively demonstrate the distribution rule of their eye positions. The multiple linear regression and principal component analysis were adopted to processing the feature parameters of human and tractor, and the prediction effect of the model was assessed according to the proportion of the driver’s eye positions from side view those were within the range of the eyellipse model. The experimental result revealed that the centroid positions and principal axis dip angle of the model differed from each other respectively, while the axis lengths were the same. The principal axis dip angle was horizontal or oblique forward-and-upward. And the tractor layout parameters had a significant impact on the centroid coordinate and principal axis dip angle of the model, while the dimension of the model was associated with the anthropometric parameters mainly. The prediction accuracy of the model exceeded 80%. The regression predictive model provides a reference tool for the visual field design of tractor displays and cab layouts.

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Head pose-free eye gaze prediction for driver attention study

Cited by 8 publications

References 15 publications

Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

Driver’s Head Pose and Gaze Zone Estimation Based on Multi-Zone Templates Registration and Multi-Frame Point Cloud Fusion

Prediction of the distribution of tractor driver’s eye positions

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